134 research outputs found

    Extracting Prior Knowledge from Data Distribution to Migrate from Blind to Semi-Supervised Clustering

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    Although many studies have been conducted to improve the clustering efficiency, most of the state-of-art schemes suffer from the lack of robustness and stability. This paper is aimed at proposing an efficient approach to elicit prior knowledge in terms of must-link and cannot-link from the estimated distribution of raw data in order to convert a blind clustering problem into a semi-supervised one. To estimate the density distribution of data, Wiebull Mixture Model (WMM) is utilized due to its high flexibility. Another contribution of this study is to propose a new hill and valley seeking algorithm to find the constraints for semi-supervise algorithm. It is assumed that each density peak stands on a cluster center; therefore, neighbor samples of each center are considered as must-link samples while the near centroid samples belonging to different clusters are considered as cannot-link ones. The proposed approach is applied to a standard image dataset (designed for clustering evaluation) along with some UCI datasets. The achieved results on both databases demonstrate the superiority of the proposed method compared to the conventional clustering methods

    Quantification of sEMG signals for automated muscle fatigue detection using nonlinear SVM

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    Fatigue is a multidimensional and subjective concept and is a complex phenomenon including various causes, mechanisms and forms of manifestation. Thus, it is crucial to delineate the different levels and to quantify selfperceived fatigue. The aim of this study was to introduce a method for automatic quantification and detection of muscle fatigue using surface EMG signals. Thus, sEMG signals from right sternocleidomastoid muscle of 9 healthy female subjects were recorded during neck flexion endurance test in Quaem hospital. Then six features in time, frequency and time- scale domains were extracted from signals. After dimensionality estimation and reduction, the SVM classifier was applied to the resulted feature vector. Then, the performance of linear SVM and nonlinear SVM with RBF kernel and the effect of show that the best accuracy is achieved using RBF kernel SVM with features using LLE criterion, were RMS, ZC and AIF. These results suggest that the selected features contained some information that could be used by nonlinear SVM with RBF kernel to best discriminate between fatigue and nonfatigue stages.    </p

    Novel mutation identification and copy number variant detection via exome sequencing in congenital muscular dystrophy.

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    BACKGROUND: Congenital muscular dystrophy type 1A (MDC1A), also termed merosin-deficient congenital muscular dystrophy (CMD), is a severe form of CMD caused by mutations in the laminin α2 gene (LAMA2). Of the more than 300 likely pathogenic variants found in the Leiden Open Variant Database, the majority are truncating mutations leading to complete LAMA2 loss of function, but multiple copy number variants (CNVs) have also been reported with variable frequency. METHODS: We collected a cohort of individuals diagnosed with likely MDC1A and sought to identify both single nucleotide variants and small and larger CNVs via exome sequencing by extending the analysis of sequencing data to detect splicing changes and CNVs. RESULTS: Standard exome analysis identified multiple novel LAMA2 variants in our cohort, but only four cases carried biallelic variants. Since likely truncating LAMA2 variants are often found in heterozygosity without a second allele, we performed additional splicing and CNV analysis on exome data and identified one splice change outside of the canonical sequences and three CNVs, in the remaining four cases. CONCLUSIONS: Our findings support the expectation that a portion of MDC1A cases may be caused by at least one CNV allele and show how these changes can be effectively identified by additional analysis of existing exome data

    BVVL/ FL: features caused by SLC52A3 mutations; WDFY4 and TNFSF13B may be novel causative genes

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    Brown-Vialetto-Van Laere (BVVL) and Fazio-Londe are disorders with amyotrophic lateral sclerosis-like features, usually with recessive inheritance. We aimed to identify causative mutations in 10 probands. Neurological examinations, genetic analysis, audiometry, magnetic resonance imaging, biochemical and immunological testings, and/or muscle histopathology were performed. Mutations in known causative gene SLC52A3 were found in 7 probands. More importantly, only 1 mutated allele was observed in several patients, and variable expressivity and incomplete penetrance were clearly noted. Environmental insults may contribute to variable presentations. Putative causative mutations in other genes were identified in 3 probands. Two of the genes, WDFY4 and TNFSF13B, have immune-related functions. Inflammatory responses were implicated in the patient with the WDFY4 mutation. Malfunction of the immune system and mitochondrial anomalies were shown in the patient with the TNFSF13B mutation. Prevalence of heterozygous SLC52A3 BVVL causative mutations and notable variability in expressivity of homozygous and heterozygous genotypes are being reported for the first time. Identification of WDFY4 and TNFSF13B as candidate causative genes supports conjectures on involvement of the immune system in BVVL and amyotrophic lateral sclerosis

    Bi-allelic variants in RNF170 are associated with hereditary spastic paraplegia.

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    Alterations of Ca2+ homeostasis have been implicated in a wide range of neurodegenerative diseases. Ca2+ efflux from the endoplasmic reticulum into the cytoplasm is controlled by binding of inositol 1,4,5-trisphosphate to its receptor. Activated inositol 1,4,5-trisphosphate receptors are then rapidly degraded by the endoplasmic reticulum-associated degradation pathway. Mutations in genes encoding the neuronal isoform of the inositol 1,4,5-trisphosphate receptor (ITPR1) and genes involved in inositol 1,4,5-trisphosphate receptor degradation (ERLIN1, ERLIN2) are known to cause hereditary spastic paraplegia (HSP) and cerebellar ataxia. We provide evidence that mutations in the ubiquitin E3 ligase gene RNF170, which targets inositol 1,4,5-trisphosphate receptors for degradation, are the likely cause of autosomal recessive HSP in four unrelated families and functionally evaluate the consequences of mutations in patient fibroblasts, mutant SH-SY5Y cells and by gene knockdown in zebrafish. Our findings highlight inositol 1,4,5-trisphosphate signaling as a candidate key pathway for hereditary spastic paraplegias and cerebellar ataxias and thus prioritize this pathway for therapeutic interventions
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